Python data.coffee函数代码示例

def test_i2v():
    """Loads the i2v network and applies it to a test image.
    """
    with tf.Session() as sess:
        net = get_i2v_model()
        tf.import_graph_def(net['graph_def'], name='i2v')
        g = tf.get_default_graph()
        names = [op.name for op in g.get_operations()]
        x = g.get_tensor_by_name(names[0] + ':0')
        softmax = g.get_tensor_by_name(names[-3] + ':0')

        from skimage import data
        img = preprocess(data.coffee())[np.newaxis]
        res = np.squeeze(softmax.eval(feed_dict={x: img}))
        print([(res[idx], net['labels'][idx])
               for idx in res.argsort()[-5:][::-1]])

        """Let's visualize the network's gradient activation
        when backpropagated to the original input image.  This
        is effectively telling us which pixels contribute to the
        predicted class or given neuron"""
        pools = [name for name in names if 'pool' in name.split('/')[-1]]
        fig, axs = plt.subplots(1, len(pools))
        for pool_i, poolname in enumerate(pools):
            pool = g.get_tensor_by_name(poolname + ':0')
            pool.get_shape()
            neuron = tf.reduce_max(pool, 1)
            saliency = tf.gradients(neuron, x)
            neuron_idx = tf.arg_max(pool, 1)
            this_res = sess.run([saliency[0], neuron_idx],
                                feed_dict={x: img})

            grad = this_res[0][0] / np.max(np.abs(this_res[0]))
            axs[pool_i].imshow((grad * 128 + 128).astype(np.uint8))
            axs[pool_i].set_title(poolname)

def test_random_enhance_any_color():
    image = data.coffee()

    for i in xrange(10):
        enhanced = random_enhance_color(image,_seed=42)
        assert (image != enhanced).any()
        assert (image.shape == enhanced.shape)
        assert (image.sum() < enhanced.sum())

def color_transformation():
    # 彩色变换
    image=data.coffee()
    brighter=np.uint8(image*0.5+255*0.5)
    darker=np.uint8(image*0.5)
    io.imshow(brighter)
    io.show()
    io.imshow(darker)
    io.show()

	def getImage(self,params):
		sigma = float(params['sigma'])
		r = float(params['red'])
		g = float(params['green'])
		b = float(params['blue'])
		image = data.coffee()
		new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)
		new_image[:,:,0] = r*new_image[:,:,0]
		new_image[:,:,1] = g*new_image[:,:,1]
		new_image[:,:,2] = b*new_image[:,:,2]
		return new_image

def main():
    """Load image, collect pixels, cluster, create segment images, plot."""
    # load image
    img_rgb = data.coffee()
    img_rgb = misc.imresize(img_rgb, (256, 256)) / 255.0
    img = color.rgb2hsv(img_rgb)
    height, width, channels = img.shape
    print("Image shape is: ", img.shape)

    # collect pixels as tuples of (r, g, b, y, x)
    print("Collecting pixels...")
    pixels = []
    for y in range(height):
        for x in range(width):
            pixel = img[y, x, ...]
            pixels.append([pixel[0], pixel[1], pixel[2], (y / height) * 2.0, (x / width) * 2.0])
    pixels = np.array(pixels)
    print("Found %d pixels to cluster" % (len(pixels)))

    # cluster the pixels using mean shift
    print("Clustering...")
    bandwidth = estimate_bandwidth(pixels, quantile=0.05, n_samples=500)
    clusterer = MeanShift(bandwidth=bandwidth, bin_seeding=True)
    labels = clusterer.fit_predict(pixels)

    # process labels generated during clustering
    labels_unique = set(labels)
    labels_counts = [(lu, len([l for l in labels if l == lu])) for lu in labels_unique]
    labels_unique = sorted(list(labels_unique), key=lambda l: labels_counts[l], reverse=True)
    nb_clusters = len(labels_unique)
    print("Found %d clusters" % (nb_clusters))
    print(labels.shape)

    print("Creating images of segments...")
    img_segments = [np.copy(img_rgb) * 0.25 for label in labels_unique]

    for y in range(height):
        for x in range(width):
            pixel_idx = (y * width) + x
            label = labels[pixel_idx]
            img_segments[label][y, x, 0] = 1.0

    print("Plotting...")
    images = [img_rgb]
    titles = ["Image"]
    for i in range(min(8, nb_clusters)):
        images.append(img_segments[i])
        titles.append("Segment %d" % (i))

    plot_images(images, titles)

def test_write_rgb(tmpdir_factory):
    img = coffee()
    filename = str(tmpdir_factory.mktemp("write").join("rgb_img.tif"))
    with Tiff(filename, "w") as handle:
        handle.write(img, method="tile")
    with Tiff(filename) as handle:
        data = handle[:]
        assert np.all(img == data[:, :, :3])

    with Tiff(filename, "w") as handle:
        handle.write(img, method="scanline")
    with Tiff(filename) as handle:
        data = handle[:]
        assert np.all(img == data[:, :, :3])

def run(dict,canload=0):
    import os.path
    if 'fname' in dict:
        filename=dict['fname']
    else:
        print("No filename given")
        exit(1)
    print("\n",filename,"============================================","\n")
    plt.ion()
    G=hamiltonian.GaussGreen(dict['ell'],0)
    no_steps=dict['no_steps']
    if isinstance(no_steps, list):
        ODE=diffeo.MultiShoot(G,1)
    else:
        ODE=diffeo.Shoot(G)  # use single shooting
    #
    ODE.set_no_steps(dict['no_steps'])
    ODE.set_landmarks(dict['landmarks_n'])
    ODE.solve()
    # plot warp
    plot_setup()
    plt.axis('equal')
    ODE.plot_warp()
    plt.savefig(filename+'warp.pdf',bbox_inches='tight')
    #
    # load test image
    #image = data.checkerboard()
    image = data.coffee()
    #
    # apply warp to image
    new_image=ODE.warp(image)
    # plotting and save to png
    plot_setup()
    plt.close()
    fig, (ax0, ax1) = plt.subplots(1, 2,
                                   figsize=(8, 3),
                                   sharex=True,
                                   sharey=True,
                                   subplot_kw={'adjustable':'box-forced'}
                                   )
    ax0.imshow(image, cmap=plt.cm.gray, interpolation='none')
    mpl.image.imsave('orig_image.png',image,cmap=plt.cm.gray)
    ax0.axis('off')
    #
    ax1.imshow(new_image, cmap=plt.cm.gray, interpolation='none')
    mpl.image.imsave('new_image.png',new_image,cmap=plt.cm.gray)
    ax1.axis('off')
    plt.show()
    print("finished.")

def test_minsize():
    # single-channel:
    img = data.coins()[20:168, 0:128]
    for min_size in np.arange(10, 100, 10):
        segments = felzenszwalb(img, min_size=min_size, sigma=3)
        counts = np.bincount(segments.ravel())
        # actually want to test greater or equal.
        assert_greater(counts.min() + 1, min_size)
    # multi-channel:
    coffee = data.coffee()[::4, ::4]
    for min_size in np.arange(10, 100, 10):
        segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
        counts = np.bincount(segments.ravel())
        # actually want to test greater or equal.
        assert_greater(counts.min() + 1, min_size)

def test_minsize():
    # single-channel:
    img = data.coins()[20:168,0:128]
    for min_size in np.arange(10, 100, 10):
        segments = felzenszwalb(img, min_size=min_size, sigma=3)
        counts = np.bincount(segments.ravel())
        # actually want to test greater or equal.
        assert_greater(counts.min() + 1, min_size)
    # multi-channel:
    coffee = data.coffee()[::4, ::4]
    for min_size in np.arange(10, 100, 10):
        segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
        counts = np.bincount(segments.ravel())
        # actually want to test greater or equal.
        # the construction doesn't guarantee min_size is respected
        # after intersecting the sementations for the colors
        assert_greater(np.mean(counts) + 1, min_size)

    def _build_expected_output(self):
        funcs = (grey.erosion, grey.dilation, grey.opening, grey.closing,
                 grey.white_tophat, grey.black_tophat)
        selems_2D = (selem.square, selem.diamond,
                     selem.disk, selem.star)

        with expected_warnings(['Possible precision loss']):
            image = img_as_ubyte(transform.downscale_local_mean(
                color.rgb2gray(data.coffee()), (20, 20)))

        output = {}
        for n in range(1, 4):
            for strel in selems_2D:
                for func in funcs:
                    key = '{0}_{1}_{2}'.format(strel.__name__, n, func.__name__)
                    output[key] = func(image, strel(n))

        return output

independently for each channel, as long as the number of channels is equal in
the input image and the reference.

Histogram matching can be used as a lightweight normalisation for image
processing, such as feature matching, especially in circumstances where the
images have been taken from different sources or in different conditions (i.e.
lighting).
"""

import matplotlib.pyplot as plt

from skimage import data
from skimage import exposure
from skimage.transform import match_histograms

reference = data.coffee()
image = data.chelsea()

matched = match_histograms(image, reference)

fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
                                    sharex=True, sharey=True)
for aa in (ax1, ax2, ax3):
    aa.set_axis_off()

ax1.imshow(image)
ax1.set_title('Source')
ax2.imshow(reference)
ax2.set_title('Reference')
ax3.imshow(matched)
ax3.set_title('Matched')

    This method computes the mean color of `dst`.

    Parameters
    ----------
    graph : RAG
        The graph under consideration.
    src, dst : int
        The vertices in `graph` to be merged.
    """
    graph.node[dst]['total color'] += graph.node[src]['total color']
    graph.node[dst]['pixel count'] += graph.node[src]['pixel count']
    graph.node[dst]['mean color'] = (graph.node[dst]['total color'] /
                                     graph.node[dst]['pixel count'])


img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)

labels2 = graph.merge_hierarchical(labels, g, thresh=35, rag_copy=False,
                                   in_place_merge=True,
                                   merge_func=merge_mean_color,
                                   weight_func=_weight_mean_color)

g2 = graph.rag_mean_color(img, labels2)

out = color.label2rgb(labels2, img, kind='avg')
out = segmentation.mark_boundaries(out, labels2, (0, 0, 0))
io.imshow(out)
io.show()

image can then be effectively performed by a mere thresholding of the HSV
channels.

.. [1] https://en.wikipedia.org/wiki/HSL_and_HSV

"""

##############################################################################
# We first load the RGB image and extract the Hue and Value channels:

import matplotlib.pyplot as plt

from skimage import data
from skimage.color import rgb2hsv

rgb_img = data.coffee()
hsv_img = rgb2hsv(rgb_img)
hue_img = hsv_img[:, :, 0]
value_img = hsv_img[:, :, 2]

fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 2))

ax0.imshow(rgb_img)
ax0.set_title("RGB image")
ax0.axis('off')
ax1.imshow(hue_img, cmap='hsv')
ax1.set_title("Hue channel")
ax1.axis('off')
ax2.imshow(value_img)
ax2.set_title("Value channel")
ax2.axis('off')

References
----------
.. [1] Xie, Yonghong, and Qiang Ji. "A new efficient ellipse detection
       method." Pattern Recognition, 2002. Proceedings. 16th International
       Conference on. Vol. 2. IEEE, 2002
"""

import matplotlib.pyplot as plt

from skimage import data, filter, color
from skimage.transform import hough_ellipse
from skimage.draw import ellipse_perimeter

# Load picture, convert to grayscale and detect edges
image_rgb = data.coffee()[0:220, 160:420]
image_gray = color.rgb2gray(image_rgb)
edges = filter.canny(image_gray, sigma=2.0,
                     low_threshold=0.55, high_threshold=0.8)

# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges, accuracy=20, threshold=250,
                       min_size=100, max_size=120)
result.sort(order='accumulator')

# Estimated parameters for the ellipse
best = result[-1]
yc = int(best[1])

from skimage.data import coffee, camera
from sklearn_theano.feature_extraction import (
    GoogLeNetTransformer, GoogLeNetClassifier)
import numpy as np
from nose import SkipTest
import os

co = coffee().astype(np.float32)
ca = camera().astype(np.float32)[:, :, np.newaxis] * np.ones((1, 1, 3),
                                                             dtype='float32')


def test_googlenet_transformer():
    """smoke test for googlenet transformer"""
    if os.environ.get('CI', None) is not None:
        raise SkipTest("Skipping heavy data loading on CI")
    t = GoogLeNetTransformer()

    t.transform(co)
    t.transform(ca)


def test_googlenet_classifier():
    """smoke test for googlenet classifier"""
    if os.environ.get('CI', None) is not None:
        raise SkipTest("Skipping heavy data loading on CI")
    c = GoogLeNetClassifier()

    c.predict(co)
    c.predict(ca)